Introduction: Chatbots and conversational AI need memory. Without conversation history, every message exists in isolation—the model can’t reference what was said before, follow up on previous topics, or maintain coherent multi-turn dialogues. But history management is tricky: context windows are limited, old messages may be irrelevant, and naive approaches quickly hit token limits. This guide […]
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Conversation Design Patterns: Building Natural Chatbot Experiences
Introduction: Effective conversational AI requires more than just calling an LLM—it needs thoughtful conversation design. This includes managing multi-turn context, handling user intent, graceful error recovery, and maintaining consistent personality. This guide covers essential conversation patterns: intent classification and routing, slot filling for structured data collection, conversation state machines, context window management, and building chatbots […]
Read more →GPT-4 Turbo and the OpenAI Assistants API: Building Production Conversational AI Systems
Introduction: OpenAI’s DevDay 2023 marked a pivotal moment in AI development with the announcement of GPT-4 Turbo and the Assistants API. These releases fundamentally changed how developers build AI-powered applications, offering 128K context windows, native JSON mode, improved function calling, and persistent conversation threads. After integrating these capabilities into production systems, I’ve found that the […]
Read more →Mastering Prompt Engineering: Advanced Techniques for Production LLM Applications
Introduction: Prompt engineering has emerged as one of the most critical skills in the AI era. The difference between a mediocre AI response and an exceptional one often comes down to how you structure your prompt. After years of working with large language models across production systems, I’ve distilled the most effective techniques into this […]
Read more →Document Processing Pipelines: From Raw Files to Vector-Ready Chunks
Introduction: Document processing is the foundation of any RAG (Retrieval-Augmented Generation) system. Before you can search and retrieve relevant information, you need to extract text from various file formats, split it into meaningful chunks, and generate embeddings for vector search. The quality of your document processing pipeline directly impacts retrieval accuracy and ultimately the quality […]
Read more →LLM Caching Strategies: From Exact Match to Semantic Similarity
Introduction: LLM API calls are expensive and slow. Caching is your first line of defense against runaway costs and latency. But caching LLM responses isn’t straightforward—the same question phrased differently should return the same cached answer. This guide covers caching strategies for LLM applications: exact match caching for deterministic queries, semantic caching using embeddings for […]
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